kodai3 / flutter_tflite

Flutter plugin for TensorFlow Lite

tflite

A Flutter plugin for accessing TensorFlow Lite API. Supports Classification and Object Detection on both iOS and Android.

Breaking changes since 1.0.0:

1. Updated to TensorFlow Lite API v1.12.0.
2. No longer accepts parameter inputSize and numChannels. They will be retrieved from input tensor.
3. numThreads is moved to Tflite.loadModel.

Installation

Add tflite as a dependency in your pubspec.yaml file.

Android

In android/app/build.gradle, add the following setting in android block.

    aaptOptions {
        noCompress 'tflite'
    }

iOS

If you get error like "'vector' file not found", please open ios/Runner.xcworkspace in Xcode, click Runner > Tagets > Runner > Build Settings, search Compile Sources As, change the value to Objective-C++;

Usage

1. Create a assets folder and place your label file and model file in it. In pubspec.yaml add:

  assets:
   - assets/labels.txt
   - assets/mobilenet_v1_1.0_224.tflite

2. Import the library:

import 'package:tflite/tflite.dart';

3. Load the model and labels:

String res = await Tflite.loadModel(
  model: "assets/mobilenet_v1_1.0_224.tflite",
  labels: "assets/labels.txt",
  numThreads: 1 // defaults to 1
);

4. See Image Classication and Object Detection below.

5. Release resources:

await Tflite.close();

Image Classification

• Output fomart:

{
  index: 0,
  label: "person",
  confidence: 0.629
}

• Run on image:

var recognitions = await Tflite.runModelOnImage(
  path: filepath,   // required
  imageMean: 0.0,   // defaults to 117.0
  imageStd: 255.0,  // defaults to 1.0
  numResults: 2,    // defaults to 5
  threshold: 0.2    // defaults to 0.1
);

• Run on binary:

var recognitions = await Tflite.runModelOnBinary(
  binary: imageToByteListFloat32(image, 224, 127.5, 127.5),// required
  numResults: 6,    // defaults to 5
  threshold: 0.05,  // defaults to 0.1
);

Uint8List imageToByteListFloat32(
    img.Image image, int inputSize, double mean, double std) {
  var convertedBytes = Float32List(1 * inputSize * inputSize * 3);
  var buffer = Float32List.view(convertedBytes.buffer);
  int pixelIndex = 0;
  for (var i = 0; i < inputSize; i++) {
    for (var j = 0; j < inputSize; j++) {
      var pixel = image.getPixel(j, i);
      buffer[pixelIndex++] = (img.getRed(pixel) - mean) / std;
      buffer[pixelIndex++] = (img.getGreen(pixel) - mean) / std;
      buffer[pixelIndex++] = (img.getBlue(pixel) - mean) / std;
    }
  }
  return convertedBytes.buffer.asUint8List();
}

Uint8List imageToByteListUint8(img.Image image, int inputSize) {
  var convertedBytes = Uint8List(1 * inputSize * inputSize * 3);
  var buffer = Uint8List.view(convertedBytes.buffer);
  int pixelIndex = 0;
  for (var i = 0; i < inputSize; i++) {
    for (var j = 0; j < inputSize; j++) {
      var pixel = image.getPixel(j, i);
      buffer[pixelIndex++] = img.getRed(pixel);
      buffer[pixelIndex++] = img.getGreen(pixel);
      buffer[pixelIndex++] = img.getBlue(pixel);
    }
  }
  return convertedBytes.buffer.asUint8List();
}

• Run on image stream (video frame):

Works with camera plugin 4.0.0. Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.

var recognitions = await Tflite.runModelOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  imageHeight: img.height,
  imageWidth: img.width,
  imageMean: 127.5,   // defaults to 127.5
  imageStd: 127.5,    // defaults to 127.5
  rotation: 90,       // defaults to 90, Android only
  numResults: 2,      // defaults to 5
  threshold: 0.1,     // defaults to 0.1
);

Object Detection

• Output fomart:

x, y, w, h are between [0, 1]. You can scale x, w by the width and y, h by the height of the image.

{
  detectedClass: "hot dog",
  confidenceInClass: 0.123,
  rect: {
    x: 0.15,
    y: 0.33,
    w: 0.80,
    h: 0.27
  }
}

SSD MobileNet:

• Run on image:

var recognitions = await Tflite.detectObjectOnImage(
  path: filepath,       // required
  model: "SSDMobileNet",
  imageMean: 127.5,     
  imageStd: 127.5,      
  threshold: 0.4,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
);

• Run on binary:

var recognitions = await Tflite.detectObjectOnBinary(
  binary: imageToByteListUint8(resizedImage, 300), // required
  model: "SSDMobileNet",  
  threshold: 0.4,                                  // defaults to 0.1
  numResultsPerClass: 2,                           // defaults to 5
);

• Run on image stream (video frame):

Works with camera plugin 4.0.0. Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.

var recognitions = await Tflite.detectObjectOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  model: "SSDMobileNet",  
  imageHeight: img.height,
  imageWidth: img.width,
  imageMean: 127.5,   // defaults to 127.5
  imageStd: 127.5,    // defaults to 127.5
  rotation: 90,       // defaults to 90, Android only
  numResults: 2,      // defaults to 5
  threshold: 0.1,     // defaults to 0.1
);

Tiny YOLOv2:

• Run on image:

var recognitions = await Tflite.detectObjectOnImage(
  path: filepath,       // required
  model: "YOLO",      
  imageMean: 0.0,       
  imageStd: 255.0,      
  threshold: 0.3,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
  anchors: anchors,// defaults to [0.57273,0.677385,1.87446,2.06253,3.33843,5.47434,7.88282,3.52778,9.77052,9.16828]
  blockSize: 32,        // defaults to 32
  numBoxesPerBlock: 5   // defaults to 5
);

• Run on binary:

var recognitions = await Tflite.detectObjectOnBinary(
  binary: imageToByteListFloat32(resizedImage, 416, 0.0, 255.0), // required
  model: "YOLO",  
  threshold: 0.3,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
  anchors: anchors,     // defaults to [0.57273,0.677385,1.87446,2.06253,3.33843,5.47434,7.88282,3.52778,9.77052,9.16828]
  blockSize: 32,        // defaults to 32
  numBoxesPerBlock: 5   // defaults to 5
);

• Run on image stream (video frame):

Works with camera plugin 4.0.0. Video format: (iOS) kCVPixelFormatType_32BGRA, (Android) YUV_420_888.

var recognitions = await Tflite.detectObjectOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  model: "YOLO",  
  imageHeight: img.height,
  imageWidth: img.width,
  imageMean: 0,         // defaults to 127.5
  imageStd: 255.0,      // defaults to 127.5
  numResults: 2,        // defaults to 5
  threshold: 0.1,       // defaults to 0.1
  numResultsPerClass: 2,// defaults to 5
  anchors: anchors,     // defaults to [0.57273,0.677385,1.87446,2.06253,3.33843,5.47434,7.88282,3.52778,9.77052,9.16828]
  blockSize: 32,        // defaults to 32
  numBoxesPerBlock: 5   // defaults to 5
);

pix2pix

Thanks to RP from Green Appers

• Run on image:

var result = await runPix2PixOnImage(
  path: filepath,       // required
  imageMean: 0.0,       // defaults to 0.0
  imageStd: 255.0,      // defaults to 255.0
);

Output:

{
  "filename": outputFile
}

• Run on binary:

var result = await runPix2PixOnBinary(
  binary: binary,       // required;
);

Output:

{
  "binary": outputBinary
}

• Run on image stream (video frame):

var result = await unPix2PixOnFrame(
  bytesList: img.planes.map((plane) {return plane.bytes;}).toList(),// required
  imageHeight: img.height, // defaults to 1280
  imageWidth: img.width,   // defaults to 720
  imageMean: 127.5,   // defaults to 0.0
  imageStd: 127.5,    // defaults to 255.0
  rotation: 90,       // defaults to 90, Android only
);

Output:

{
  "binary": outputBinary
}

Demo

• Classification and object detection

  Refer to the example.

• Real-time detection

  Refer to flutter_realtime_detection.